AIMC Topic: Humans

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Towards key genes identification for breast cancer survival risk with neural network models.

Computational biology and chemistry
Breast cancer, one common malignant tumor all over the world, has a considerably high rate of recurrence, which endangers the health and life of patients. While more and more data have been available, how to leverage the gene expression data to predi...

Automatic diagnosis for adenomyosis in ultrasound images by deep neural networks.

European journal of obstetrics, gynecology, and reproductive biology
OBJECTIVE: To present a new noninvasive technique for automatic diagnosis of adenomyosis, using a novel end-to-end unified network framework based on transformer networks.

Structure and position-aware graph neural network for airway labeling.

Medical image analysis
We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are ex...

Harnessing artificial intelligence microscopy to improve diagnostics for soil-transmitted helminthiasis and schistosomiasis: a review of recent advances and future pathways.

Current opinion in infectious diseases
PURPOSE OF REVIEW: This opinion piece aims to explore the transformative potential of integrating artificial intelligence with digital microscopy to enhance diagnostics for soil-transmitted helminthiasis (STH) and schistosomiasis (SCH), two pervasive...

Deep-learning based analysis of in-vivo confocal microscopy images of the subbasal corneal nerve plexus' inferior whorl in patients with neuropathic corneal pain and dry eye disease.

The ocular surface
PURPOSE: To evaluate and compare subbasal corneal nerve parameters of the inferior whorl in patients with dry eye disease (DED), neuropathic corneal pain (NCP), and controls using a novel deep-learning-based algorithm to analyze in-vivo confocal micr...

The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery.

The Canadian journal of cardiology
Artificial Intelligence (AI) has greatly affected our everyday lives and holds great promise to change the landscape of medicine. AI is particularly positioned to improve care for the increasingly complex patients undergoing cardiac surgery using the...

Machine learning-driven diagnosis of multiple sclerosis from whole blood transcriptomics.

Brain, behavior, and immunity
Multiple sclerosis (MS) is a neurological disorder characterized by immune dysregulation. It begins with a first clinical manifestation, a clinically isolated syndrome (CIS), which evolves to definite MS in case of further clinical and/or neuroradiol...

Data-driven prediction of spinal cord injury recovery: An exploration of current status and future perspectives.

Experimental neurology
Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is a promising approach to enhance the prediction of recovery trajectories, but its i...

Transforming Health Care Landscapes: The Lever of Radiology Research and Innovation on Emerging Markets Poised for Aggressive Growth.

Journal of the American College of Radiology : JACR
Advances in radiology are crucial not only to the future of the field but to medicine as a whole. Here, we present three emerging areas of medicine that are poised to change how health care is delivered-hospital at home, artificial intelligence, and ...